Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

Zhu, Chengtao; Chang, Yau-Zen

doi:10.3390/app9153122

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…To show the competitiveness of the proposed algorithm, we also performed a comparison with other published methods, MC-CNN-acrt [14], PSMNet_ROB [22], JMR [24], SGBMP [23], MC-CNN-WS [29], HGIF [30], and LS_ELAS [31]. The quantitative comparison performed as illustrated in Table 1 and 2.…”

Section: Resultsmentioning

confidence: 99%

Stereo Matching Algorithm Based on Hybrid Convolutional Neural Network and Directional Intensity Difference

Hamid¹,

Manap²,

Hamzah³

et al. 2021

IJETAE

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Fundamentally, a stereo matching algorithm produces a disparity map or depth map. This map contains valuable information for many applications, such as range estimation, autonomous vehicle navigation and 3D surface reconstruction. The stereo matching process faces various challenges to get an accurate result for example low texture area, repetitive pattern and discontinuity regions. The proposed algorithm must be robust and viable with all of these challenges and is capable to deliver good accuracy. Hence, this article proposes a new stereo matching algorithm based on a hybrid Convolutional Neural Network (CNN) combined with directional intensity differences at the matching cost stage. The proposed algorithm contains a deep learning-based method and a handcrafted method. Then, the bilateral filter is used to aggregate the matching cost volume while preserving the object edges. The Winner-Take-All (WTA) is utilized at the optimization stage which the WTA normalizes the disparity values. At the last stage, a series of refinement processes will be applied to enhance the final disparity map. A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. This dataset provides images with the characteristics of low texture area, repetitive pattern and discontinuity regions. The average error produced for all pixel regions is 8.51%, while the nonoccluded region is 5.77%. Based on the experimental results, the proposed algorithm produces good accuracy and robustness against the stereo matching challenges. It is also competitive with other published methods and can be used as a complete algorithm

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Section: Resultsmentioning

confidence: 99%

Stereo Matching Algorithm Based on Hybrid Convolutional Neural Network and Directional Intensity Difference

Hamid¹,

Manap²,

Hamzah³

et al. 2021

IJETAE

View full text Add to dashboard Cite

show abstract

“…Zhu et al [27] Apply edge-preserving guided-Image-filtering (GIF) at Cost volume regularization different resolutions on multi-scale stereo matching.…”

Section: Cost Volume Regularizationmentioning

confidence: 99%

Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Xiao

Yamane

2021

Sensors

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Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66G MAdd (Multiply-Add operations) to 69.03G MAdd (198.47G MAdd to 10.84G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.

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“…Zhu Chengtao et al [4] reported on "Hierarchical Guided Image Filtering for Efficient Stereo Matching". Stereo matching is complicated by the uneven distribution of textures on image pairs.…”

Section: Topics Of Selected Papersmentioning

confidence: 99%